MapR Ups Its Hadoop Game with Databricks' Spark

For now, MapR seems to be sitting on the sidelines of the “My Hadoop distro is better than yours” game, and as Jack Norris, the company’s CMO puts it, “we’re concentrating on doing what’s best for our customers.” (Who wouldn’t say something similar?)

It’s with that in mind that they announced today the addition of the Apache Spark stack to their distribution. Norris says Spark will add speed, programming ease and real time processing abilities to their current offering.

In addition to that, Apache Spark has more than 90 developers contributing to the open source project. Add in Apache Shark (Spark on Hive) contributors and you may just have the largest Apache contributors’ community on the planet.

Why does this matter? Because the technology will evolve faster as a result. Databricks, the shepherd behind the Apache Spark project, says that it’s not only growing at an incredibly rapid rate, but that it is also being extremely careful with the certification process -- it is not about handing over a check and passing an easy test.

What's In It for the Enterprise?

Why does all of this matter? Because the Spark stack as a whole (MapR is the only Hadoop vendor thus far to support it in its entirety) changes what enterprises can do with big data processing.

Norris says that MapR clients are now using its big data offering to realize both analytical and operational advantages, such as being able to detect fraud as it is occurring (which wouldn’t be possible to do with Map Reduce because it’s too slow).

While we had MapR on the line, we figured we’d ask a question our audience has asked us -- namely “with Cloudera’s $900 million of new funding and Hortonworks’ $100 million, don’t you also need money to grow?”

Norris said that with 500 paid customers the company is profitable and able to continue being successful from its current position. (Or until they, too, make an announcement, say we.)

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